Neither "AI Bubble Burst" Nor "Enterprise Software is Dead" Extremes Seem Likely

If you are an investor in either enterprise software; high-performance data centers or both, you face a truly-odd scenario, where, at the same time, we are warned that an “AI investment bubble” is underway at the same time that enterprise software (or software as a service generally) is at risk of extreme disruption from AI. 

One might argue software disruption or a high-performance computing “bubble” are conceivable, but not both together, as they tend to be mutually exclusive outcomes. 

In other words, the logical problem is that if AI really does not produce financial returns that justify the huge investments, then perhaps enterprise software actually is not badly disrupted.

On the other hand, if enterprise software really is disrupted, then the AI investments ought to have paid off, as they have proven consequential enough to destroy much of the value of enterprise software. 

And it always is possible that neither extreme scenario develops. In that case, perhaps high-performance computing facilities do produce a reasonable return, if perhaps not a “once in a lifetime” extraordinary return. 

And perhaps enterprise software adapts to AI, reshaping its business model successfully, though perhaps suffering a loss of profit margins in some instances. 

It’s the sort of scenario we often encounter. Some worry that Nvidia’s high margins or gross sales will be affected by competitors, ranging from AMD to in-house chip replacements from Alphabet or AWS, for example. 

And, often, there is some degree of displacement, but not ruin. And while we cannot know the timing or extent of disruption (so we must choose our “buy” prices), the extreme scenarios tend not to emerge. 

Netflix might not ultimately acquire Warner Brothers Discovery. Paramount might get the asset. Is it the “end” for Netflix? Not many would agree. Might Netflix have to find other ways to keep growing revenue? 

Yes. 

But apocalypse is not a likely outcome. 

Prudent investors will attempt to take advantage of market mispricing. And some degree of mispricing is likely. Volatility can be “your friend.” 

Scenario	The "Bear Case"	Why it invalidates the other Bear Case
AI Infrastructure Fails	Data centers sit empty; no ROI.	SaaS is Safe: If AI isn't useful enough to pay for compute, it isn't powerful enough to replace the complex workflows and "systems of record" that SaaS provides.
SaaS is Disrupted	AI agents replace apps and "per-seat" pricing.	Infra is Validated: To disrupt SaaS, AI must be doing the work. This requires massive, constant compute. The "Capex" wasn't a waste; it was the foundation of the new economy.
The "Middle Path"	AI becomes a feature, not a substitute.	Both Win/Lose: This is the boring reality where SaaS companies integrate AI and raise prices, and Infra gets a steady, if not "hyperscale," return.

We are never free from the risk of picking the “wrong” assets in a momentarily-depressed asset class, or making the wrong bets on whole categories of assets. 

But it seems to me unlikely the extreme “AI infra bubble” burst or “enterprise software is dead” theses will happen.

Some bad bets will be made, no doubt. That always happens. But do we really want to bet against the magnitude of impact?

Disintermediation, Again

Disintermediation, the removal of elements in a value chain particularly related to distribution, was a primary effect of the internet. 

Artificial intelligence should cause even more disintermediation, as processes and value chain roles dependent on information asymmetry are removed. 

source: WallStreetMojo

Worse, some might fear, there might be no natural restorative mechanism similar to the boom-bust; recession-recovery; supply-demand cycles we commonly see in economies. 

Instead, in a worst-case scenario, AI continually depresses consumer spending (which represents about 70 percent of all economic activity) as AI leads to layoffs, which leads to less consumer spending, which increases the necessity of relying on AI to protect firm profit margins. 

Industry	How AI Removes Friction/Asymmetry	Expected Impact
Real Estate Brokerage	AI agents instantly access MLS data, decades of transaction records, valuations, and matching—eliminating agents' knowledge advantage and buyer/seller search friction.	Commissions drop sharply (e.g., from 5-6% to under 1%); many deals close agent-free or via AI "agent-on-agent"; widespread disintermediation and value loss for traditional firms.
Insurance Brokerage/Underwriting	AI enables instant policy comparison, re-shopping, risk prediction via vast data, and fraud detection—eroding inertia-based renewals and broker expertise.	15-20% premium loss from passive renewals; brokerage spreads compress; shift to direct/AI-driven models; broker selloffs (e.g., triggered by tools like Insurify).
Wealth Management / Financial Advisory	AI delivers personalized portfolio advice, tax strategies, and real-time market analysis—democratizing what once required expensive human experts and proprietary insights.	Erosion of 1% AUM or high advisory fees for basic services; "basic financial advisory" faces collapse; robo-advisors and AI tools dominate routine work.
Legal Services (Routine)	AI automates contract drafting, precedent research, due diligence, and basic navigation of laws—reducing asymmetry between lawyers and clients on standard matters.	Commoditization of routine work; reduced demand for junior/entry-level roles; faster/cheaper services pressure billable hours and firm margins.
Travel Booking Platforms	AI agents autonomously assemble custom itineraries (flights, hotels, etc.) faster/cheaper than platforms, bypassing search friction and default inertia.	Margin compression for intermediaries; habitual booking models disrupted; platforms lose value as direct AI routing prevails.
Logistics / Freight Brokerage	AI optimizes routes, matches shippers/carriers directly, and forecasts with superior data—eliminating broker coordination friction and information edges.	Broker fees collapse; rapid selloffs (e.g., truck brokers like RXO); shift to automated direct platforms.
Consulting / Professional Advisory Services	AI handles research, data analysis, and initial recommendations—eroding human expertise moats and proprietary knowledge asymmetry.	Reduced fees for routine/cognitive tasks; white-collar headcount cuts; productivity gains but revenue destruction for traditional models.
Ratings Agencies / Index Providers / Credit Checks	AI aggregates and analyzes public/proprietary data at scale—undermining exclusive research advantages and manual verification friction.	Proprietary edges erode; commoditization of ratings/indexing; lower barriers and pricing pressure.

Payments also are an obvious place to look for changes, though the changes might come from use of blockchain-based payment processors offering stablecoins.

source: Citrini Research

 

Software Firms Have Wanted "Outcomes-Based" Pricing for Decades: AI Means They Might Finally Get It

Private equity firms have poured hundreds of billions of dollars into enterprise software firms over the last few decades, on the assumption that terminal values and growth rates were sizable. 

But artificial intelligence has raised existential questions about enterprise software valuations because it raises the issue of positive feedback loops, which are inherently stability disrupting. 

A positive feedback loop is a process where the output of a system amplifies or intensifies the initial stimulus, driving the system further away from its original state. A negative feedback loop, on the other hand, allows the system to adjust. 

source: Citrini Research 

Unlike negative feedback that maintains stability, positive loops often cause exponential change in a single direction, without the corrective feedback that allows the system to stabilize itself.

In the context of what AI might do, it could, in principle, reduce employment. Reduced employment might lead to less consumer spending. Less consumer spending should put pressure on gross revenues and profit margins.

That might increase reliance on AI to reduce operating costs, which in turn further reduces employment and spending, and therefore firm revenues and profits. 

If AI agents can write code and execute complex workflows autonomously, what happens to the software companies built on charging per human "seat" or user? 

What happens to cash flows, and the valuation models built on those cash flows?

How much enterprise software value creation will happen in the future, compared to other segments of the market? And what does that imply for asset holding periods and exits?

Area of PE Operation	The Historical SaaS Era	The Current AI-Disrupted Reality
Valuations & Multiples	Software enjoyed near-automatic, premium revenue multiples (often 20x+ ARR) due to reliable recurring revenue and low customer churn.	A fundamental re-rating is underway. Investors are questioning long-term defensibility, dragging exit expectations down (e.g., closer to 15x EBITDA). PE firms are facing potential markdowns on legacy SaaS portfolios.
Due Diligence	Focus was heavily weighted on Net Revenue Retention (NRR), customer acquisition costs (CAC), and the "stickiness" of workflows.	Firms are now commissioning aggressive "AI vulnerability audits." The core question is whether a target's product is an easily replaceable interface, or if it possesses proprietary, structured data that AI agents need to function safely.
Value Creation (Operations)	Growth relied on expanding sales/marketing teams and steadily increasing the number of licensed human users per client account.	Operating teams are forcing portfolio companies to pivot away from seat-based pricing toward outcome-based or usage-based models. Genuine AI integration is now required for survival, not just for marketing.
Leverage & Private Credit	Software LBOs were built on highly secure, predictable cash flows, allowing PE firms to utilize significant leverage.	Private credit markets are showing stress (evidenced by the recent gating of tech-heavy credit funds in early 2026). Lenders fear that if AI shrinks a software company's user base, cash flows will contract, increasing default risks.
Exit Environments	Shorter hold periods (3–5 years) with reliable exits via public markets (IPOs) or sales to strategic buyers/larger PE funds.	Hold periods are extending. Exits are incredibly difficult to execute without hard proof that a portfolio company has successfully evolved into an AI-augmented platform rather than a legacy SaaS tool facing obsolescence.

The sort of interesting issue is that lots of observers have wanted to shift pricing from a more-commodity-like “seats” model to a “success-based” or “outcomes” pattern, the thinking being that this approach provides higher margins and perceived product value. 

Such an approach, in principle, makes more revenue sources available to suppliers: not just a share of the information technology budget but a share of saved labor costs; revenue shrinkage; customer acquisition; churn; marketing or other operations costs. 

Company, Product	Function	Outcome Metric	Pricing Mechanism	Strategic Benefit
Intercom Fin	AI customer support	Tickets resolved	~$0.99 per successful resolution	Aligns price with support deflection value; drives product improvement (Pragmatic Institute - Corporate)
Zendesk AI agents	Customer service	Successful resolutions	Charge per resolved ticket	Hybrid model linking AI value to outcomes (L.E.K. Consulting)
Chargeflow	Payments / fintech	Chargebacks recovered	% of recovered revenue	Captures share of direct financial benefit (rezoomex)
Riskified	E-commerce fraud	Approved fraud-free transactions	Charge per approved transaction	Direct link between trust and revenue protection (L.E.K. Consulting)
Vendr	SaaS procurement	Savings negotiated	% of savings	Monetizes cost reduction achieved (tanayj.com)
ServiceNow pilots	Workflow automation	Efficiency improvements	Full payment only if targets met	Demonstrates ROI in enterprise automation (Monetizely)
InsideSales / XANT	Sales acceleration	Conversion & velocity improvements	Performance-linked pricing	Aligns cost with revenue impact (Monetizely)
HubSpot performance tiers	Marketing automation	Campaign performance metrics	Discounts tied to results	Incentivizes effective usage (Monetizely)
Hitachi Rail “trains-as-a-service”	Industrial IoT	On-time performance	Payment linked to punctuality	Transfers reliability risk to vendor (Pragmatic Institute - Corporate)
Identity verification (iDenfy)	Fraud / KYC	Verified users	Charge per approved identity	Clear, measurable success metric (L.E.K. Consulting)

To use a simple example, instead of selling shovels, one sells “holes.” In some ways, it is similar to the shift from “product” pricing to “services” pricing. Instead of selling a car, one sells transportation services, with recurring fees rather than one upfront purchase. 

Business Function	The "Outcome" Being Sold	Real-World/Current Examples	Pricing Mechanism
Customer Support	A successfully resolved customer inquiry.	Intercom (Fin AI), Zendesk	No charge for "chats"; you only pay (e.g., $0.99) when the AI solves the issue without human intervention.
Legal & Compliance	A completed, filing-ready legal document.	EvenUp, Harvey	Instead of a monthly license for paralegals, firms pay per AI-generated demand letter or contract audit completed.
Sales & Marketing	A "Sales Qualified Lead" (SQL) or booked meeting.	11x.ai, Salesforce (Agentforce)	Companies pay for the meeting booked by the AI agent, rather than paying for a seat for a human Sales Development Rep (SDR).
Fintech / Payments	A recovered chargeback or fraud-free transaction.	Chargeflow, Riskified	The vendor takes a percentage of the money recovered or a fee only for transactions that are proven to be legitimate.
Cloud Operations	Hard-dollar savings on infrastructure bills.	Viewnear, ProsperOps	The software is free or low-cost; the vendor takes a 20-30% "success fee" of the actual savings generated on the client’s AWS/Snowflake bill.
Supply Chain	A successfully renegotiated vendor contract.	Pactum, Luminous	The AI agent autonomously negotiates with thousands of "long-tail" vendors; the fee is a share of the cost-reduction achieved.

Up to this point, such outcomes-based pricing has been quite difficult to implement. AI might make it imperative.